Value in Health最新文献

Objectives: This report details the development, validation, and implementation of a digital health decision platform focused on guideline- and policy-congruent pain management pathways in the US Military Health System (MHS).

Methods: A discrete-event, patient-level model based on the Defense Health Agency Stepped Care Model for Pain included 11 nodes (eg, care escalation, emergency encounters) parameterized with cross-validated methods and verified through configuration-controlled tests. Validation incorporated clinician review, guideline and policy alignment, and population statistic comparisons. A synthetic cohort with common pain conditions was simulated to demonstrate model interpretability and policy relevance.

Results: Many statistical approaches were incorporated into the simulation-based decision support platform. A patient generator produced simulated patients representative of the population based on 2016 to 2019 data. Statistical models determined the next encounter type (eg, primary care, physical therapy), system of care (eg, civilian versus MHS facilities), primary care encounters until secondary or tertiary care, days between appointment request and completion, procedural pain intervention receipt (eg, injections), prescription receipt, and end of pain episode. Several interrelated outcomes were captured, including opioid prescription receipt, emergency room utilization, and pain episode recurrence. Next, the capabilities necessary for modeling counterfactuals (hypothetical conditions) were developed to simulate outcomes relevant for individual and health system decision support.

Conclusions: The resulting simulation-based digital decision support platform enables testing for counterfactual policy and resource allocation decisions as it relates to chronic pain management in the MHS. Future work is needed to apply and further validate the platform.

Surrogate endpoints are frequently used as primary outcomes in clinical trials. This is appropriate when they are validated for their ability to predict clinical benefit measured on patient-relevant target outcome(s). Such validation is often lacking, thus increasing uncertainty in the decision-making process of regulatory bodies, health technology assessment (HTA) agencies and payers. This ISPOR Task Force Report provides recommendations on best practices for surrogate endpoint evaluation for HTA decision-making. It covers methods that address the three levels of evidence for surrogate endpoint validation described in several methodological guidelines: 1) association between treatment effects on the surrogate and the target outcome, 2) association between the surrogate and the target outcome, and 3) biological plausibility. Statistical methods for surrogate endpoint evaluation include meta-analytic approaches using individual participant data or aggregate data. Multivariate meta-analytic models are recommended, as they account for the within-study correlation and estimation errors. Issues with limited data and generalisability might be addressed through Bayesian approaches for information-sharing from different treatments, treatment classes or indications. Real-world data can complement randomised controlled trial data, especially in rare diseases, but require careful consideration of underlying bias. For plausibility of health economic modelling, the surrogacy analysis and the health economic model should be aligned. The modelled time course of surrogate and target outcomes per treatment arm as well as the modelled relative effects should be reported to assess plausibility. Parameter and structural uncertainty in surrogate relationships can be explored through scenario analyses, probabilistic sensitivity analyses, value of information analyses and threshold analysis techniques.

Objectives: South Korea has expanded insurance reimbursement for brain magnetic resonance imaging (MRI) under its single-payer national health insurance system. This study assessed the nationwide impact of this policy change on the diagnostic and management-related utility of brain MRI and overall diagnostic efficiency.

Methods: Nationwide claims data for brain MRI scans performed for headaches or dizziness between 2015 and 2022 were analyzed. Patients with clinically significant imaging findings (PCSIFs) were defined as those receiving follow-up observation or treatment after a new diagnosis. Low-value imaging cases, including those were later subject to selective reimbursement, were also identified. Efficiency was calculated as the number of MRI scans required to obtain one additional PCSIF across the prepolicy, expansion, and adjustment periods.

Results: The annual MRI volume increased from a mean of 93 694 before implementation to 716 085 during expansion, and then declined to 630 705 during the adjustment period. The use of MRI for headaches or dizziness increased by 1402%. PCSIFs exceeded the expected trends by 12 423 cases (26.6%) during expansion and 7808 cases (13.1%) during adjustment. Low-value imaging cases accounted for 45.4% of MRI scans during expansion. An average of 22 to 23 low-value MRI examinations were performed for each additional patient with diagnostically or clinically actionable findings. Overall, the policy markedly increased MRI use, while nearly half of additional scans were low value.

Conclusions: Expanded reimbursement without strict clinical criteria increased MRI use but reduced diagnostic efficiency. Policies should be guided by evidence-based indications to ensure efficient resource utilization and achieve clinically meaningful outcomes.

In late 2025, the White House announced new Most Favored Nation (MFN) pricing agreements for the glucagon-like peptide-1 receptor agonist class, including 3 semaglutide products, establishing substantially lower prices for Medicare and Medicaid. Shortly after, the Centers for Medicare and Medicaid Services released Maximum Fair Prices (MFPs) for selected drugs under IPAY 2027, revealing semaglutide prices that differed from the MFN prices and from earlier assumptions used in prior economic evaluations, including our prior article. Using previously published forecasting methods, we updated our 10-year (2026-2035) Medicare spending estimates for semaglutide across all US Food and Drug Administration (FDA)-approved indications under both the newly announced MFP and MFN price structures. Incorporating revised 30-day MFPs for Ozempic, Rybelsus, and Wegovy, as well as patient cost-sharing assumptions and future generic entry, we now estimate Medicare savings of $463 million under base-case MFP conditions, with alternative uptake scenarios producing $328 to $599 million in savings and up to $1.78 billion with loss-of-exclusivity assumptions. Using the lower MFN price of $245 per month and a $600 annual patient copay, estimated Medicare savings increase substantially to $1.76 billion, ranging from $1.03 to $2.50 billion across uptake scenarios and reaching $2.63 billion with generic entry. These findings highlight the significant fiscal impact of recent price negotiations and underscore uncertainties regarding the durability and future scope of MFN-based drug pricing arrangements.

Objectives: To systematically review empirical evidence on the prevalence and influence of conflicts of interest (COIs) among members and public speakers of US Food and Drug Administration (FDA) advisory committees.

Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we searched MEDLINE, PubMed, and Cochrane Library from inception to November 2024 for peer-reviewed studies reporting quantitative data on COIs in FDA advisory committees. Eligible studies examined prevalence, type, or impact of COIs among voting members or public speakers. Data extraction and quality assessment were conducted independently by 2 reviewers using the Joanna Briggs Institute checklist for cross-sectional studies.

Results: Eighteen studies published between 2006 and 2022 were included, covering committee activity from 1997 to 2022. COIs among voting members ranged from 15% to over 70% of meetings, whereas 25% of public speakers disclosed financial COIs, most commonly consulting fees, research funding, and stock ownership. Evidence linking member COIs to voting outcomes was mixed, with some studies finding no significant association. In contrast, public speakers with financial COIs were consistently more likely to deliver favorable testimony, with odds ratios ranging from 3.0 to 6.1. Several studies suggested a decline in member COI prevalence after the 2007 FDA Amendments Act, but no similar trend was observed among public speakers.

Conclusions: COIs remain prevalent in FDA advisory committees, particularly among public speakers, for which they are strongly associated with favorable testimony. These findings underscore the need for stronger and more consistent COI disclosure and management policies that include both committee members and public speakers to protect decision-making integrity.

Objectives: This systematic review assessed the scope, reporting quality, and methodological risk of bias of health economic evaluations (HEEs) of medical artificial intelligence (AI) technologies, alongside the technological maturity of the AI systems assessed.

Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines, 6 databases were searched through April 2025 for studies reporting economic outcomes of AI applications in healthcare. Reporting quality was evaluated using the AI-specific update of the Consolidated Health Economic Evaluation Reporting Standards checklist, methodological risk of bias using the ECOBIAS framework, and AI maturity using the Clinical Machine Learning Readiness Level (1-9). Inclusion of implementation and operational costs was examined, as well as their association with AI maturity.

Results: A total of 117 studies were included, with most published after 2021. Reporting quality was generally suboptimal, and ECOBIAS assessments highlight recurring risks of bias, particularly regarding incomplete cost inclusion, limited data transparency, inadequate uncertainty analysis, and insufficient model validation. Most studies evaluated AI tools at early development stages (63% at Clinical Machine Learning Readiness Level 4-5), with limited real-world validation. Although the majority of studies reported cost savings or cost-effectiveness, key cost categories were frequently omitted: only 28% included implementation costs, and 57% reported operational costs.

Conclusions: Despite frequent claims of economic benefit, current HEEs of medical AI are constrained by limited reporting quality, risk of bias, and evaluations of immature technologies. Future HEEs should explicitly report technological maturity, incorporate full cost components, and use rigorous methods. Robust evaluations conducted at higher readiness levels are also needed to generate reliable evidence for policy making, reimbursement decisions, and responsible implementation.

Objectives: Patients' time spent receiving care incurs an opportunity cost, which ought to be considered when conducting an economic evaluation from a societal perspective. Instruments for capturing time-related costs are presently lacking, especially for children and young people (CYP). To address this gap, we developed and pretested the Children and Young People's Time-Use Questionnaire for use in Economic Evaluation (CYP-TUQEE), producing versions for direct completion by CYP aged 11 to 17 years, and proxy completion by parents/carers of CYP aged up to 10 years.

Methods: The CYP-TUQEE was developed using an iterative process involving scoping reviews, consultation with a Working Group of experts, and pretesting through think-aloud interviews with 20 CYP and 9 parents/carers. This process aimed to produce a comprehensive, adaptable questionnaire that is not onerous to complete by CYP or parents/carers within the target age ranges.

Results: Participants engaged well with the think-aloud process and provided feedback to inform the development of a novel, standardized instrument to facilitate the collection and inclusion of resource- and time-use data for pediatric economic evaluations. Feedback indicates that the CYP-TUQEE is easy to complete, clear, and ready for additional validation.

Conclusions: The CYP-TUQEE addresses a prominent gap by providing an accessible tool for resource-use and time-use data collection, tailored to CYP. Inclusion of patient time costs can assist in decision making and ensure prioritization of interventions respectful of patients' time. Future research will involve additional testing of the CYP-TUQEE in a real-world setting for further validation and refinement, and elicitation of a value ('unit cost') for CYP's time.